Data-Driven Optimization and Decision-Making for Sustainable Manufacturing and Production Scheduling
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 177
Special Issue Editors
Interests: industrial artificial intelligence; large models; machine learning
Interests: assembly; human-centric manufacturing; human-machine collaboration
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Sustainable manufacturing has emerged as a pivotal strategic direction for the global manufacturing industry, aiming to balance operational efficiency, environmental sustainability, and economic viability, while production scheduling acts as the core operational link to materialize this goal. With the rapid advancement of industrial artificial intelligence, large models, machine learning and operations research methodologies, data-driven optimization and decision-making technologies are revolutionizing the traditional paradigm of sustainable manufacturing and production scheduling, providing intelligent, dynamic and precise solutions to address challenges such as low-carbon production, resource optimization and efficient scheduling in the manufacturing sector.
We are pleased to invite you to submit original research and practical application papers to this Special Issue dedicated to Data-Driven Optimization and Decision-Making for Sustainable Manufacturing and Production Scheduling. This issue aims to build a high-quality academic exchange platform, gather cutting-edge research achievements and industrial practice experiences in this field, and promote the in-depth integration of data-driven technologies with sustainable manufacturing and production scheduling.
The topics of interest include, but are not limited to:
- Data-driven optimization algorithms for production scheduling;
- AI-empowered decision-making models for sustainable manufacturing;
- Large models and machine learning applications in industrial scheduling;
- Low-carbon and resource-efficient manufacturing optimization;
- Dynamic scheduling and real-time decision-making in smart manufacturing;
- Operations research for sustainable production systems;
- Industrial case studies of data-driven manufacturing decision-making;
- Intelligent optimization of sustainable supply chain and production scheduling;
- Emerging Paradigms in Manufacturing and Scheduling Enabled by Large Language Models.
Dr. Junkai Wang
Dr. Xi Vincent Wang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- data-driven optimization
- sustainable manufacturing
- production scheduling
- AI-empowered decision making
- industrial artificial intelligence
- machine learning
- large language models
- operations research
- smart manufacturing
- low-carbon production
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